Comments: For the paper we need angles Who is using DTC? - dtc by 181 participants only (russian group) What are the differences? GK items about familiarity -- Analysis around themes High curious versus low curious Relation between curious and concerned participants - Exactly how shared is the group? (VENN DIAGRAMS)
maindf = megadf[megadf['Variable'] == 'Class_X'] maindf.drop('Variable', axis=1, inplace=True) maindf.drop('Group', axis=1, inplace=True) maindfgp_8_9 = pd.merge(sdf, maindf, on = 'id')gp_8_9['Option'] = gp_8_9['Option_y'] +' '+ gp_8_9['Option_x'] gp_n = gp_8_9[['id', 'Description_x','Option', 'Group', 'Variable']].copy() gp_n.columns = ['id', 'Description_x','Option', 'Group', 'Variable'] gp_n
class_x = BNdf[BNdf['Group'] == '77'] all_items_x = BNdf[BNdf['Group'] != '77']all_items_xgiant_BN = pd.merge(class_x, all_items_x, on='id')
giant_BN['Option'] = giant_BN['Option_x']+' '+giant_BN['Option_y']GBN = giant_BN[['id', 'Description_y', 'Option', 'Group_y', 'Variable_y']] GBN.columns = ['id', 'Description', 'Option', 'Group', 'Variable']
# saving as tsv file grnxn.to_csv('ALL_GR_network.tsv', sep="\t")
I am looking at relationships between all GK items common across 8 collections to see how it changes participants opinions and who should decide on newborn genetic screening.
{'GK Score': 78, 'Gender': 79, 'Age': 80, 'Confidence in GK': 81, 'Related/ Not related to law': 82, 'Students/ Non Students': 83, 'Law or Non Law Students and Non Students': 84, 'Concern': 85, 'Genetic Curiosity': 86}